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<h1 id="sec_name">
<span data-if="hdevelop" style="display:inline;">create_class_mlp</span><span data-if="c" style="display:none;">T_create_class_mlp</span><span data-if="cpp" style="display:none;">CreateClassMlp</span><span data-if="dotnet" style="display:none;">CreateClassMlp</span><span data-if="python" style="display:none;">create_class_mlp</span> (算子名称)</h1>
<h2>名称</h2>
<p><code><span data-if="hdevelop" style="display:inline;">create_class_mlp</span><span data-if="c" style="display:none;">T_create_class_mlp</span><span data-if="cpp" style="display:none;">CreateClassMlp</span><span data-if="dotnet" style="display:none;">CreateClassMlp</span><span data-if="python" style="display:none;">create_class_mlp</span></code> — Create a multilayer perceptron for classification or regression.</p>
<h2 id="sec_synopsis">参数签名</h2>
<div data-if="hdevelop" style="display:inline;">
<p>
<code><b>create_class_mlp</b>( :  : <a href="#NumInput"><i>NumInput</i></a>, <a href="#NumHidden"><i>NumHidden</i></a>, <a href="#NumOutput"><i>NumOutput</i></a>, <a href="#OutputFunction"><i>OutputFunction</i></a>, <a href="#Preprocessing"><i>Preprocessing</i></a>, <a href="#NumComponents"><i>NumComponents</i></a>, <a href="#RandSeed"><i>RandSeed</i></a> : <a href="#MLPHandle"><i>MLPHandle</i></a>)</code></p>
</div>
<div data-if="c" style="display:none;">
<p>
<code>Herror <b>T_create_class_mlp</b>(const Htuple <a href="#NumInput"><i>NumInput</i></a>, const Htuple <a href="#NumHidden"><i>NumHidden</i></a>, const Htuple <a href="#NumOutput"><i>NumOutput</i></a>, const Htuple <a href="#OutputFunction"><i>OutputFunction</i></a>, const Htuple <a href="#Preprocessing"><i>Preprocessing</i></a>, const Htuple <a href="#NumComponents"><i>NumComponents</i></a>, const Htuple <a href="#RandSeed"><i>RandSeed</i></a>, Htuple* <a href="#MLPHandle"><i>MLPHandle</i></a>)</code></p>
</div>
<div data-if="cpp" style="display:none;">
<p>
<code>void <b>CreateClassMlp</b>(const HTuple&amp; <a href="#NumInput"><i>NumInput</i></a>, const HTuple&amp; <a href="#NumHidden"><i>NumHidden</i></a>, const HTuple&amp; <a href="#NumOutput"><i>NumOutput</i></a>, const HTuple&amp; <a href="#OutputFunction"><i>OutputFunction</i></a>, const HTuple&amp; <a href="#Preprocessing"><i>Preprocessing</i></a>, const HTuple&amp; <a href="#NumComponents"><i>NumComponents</i></a>, const HTuple&amp; <a href="#RandSeed"><i>RandSeed</i></a>, HTuple* <a href="#MLPHandle"><i>MLPHandle</i></a>)</code></p>
<p>
<code>void <a href="HClassMlp.html">HClassMlp</a>::<b>HClassMlp</b>(Hlong <a href="#NumInput"><i>NumInput</i></a>, Hlong <a href="#NumHidden"><i>NumHidden</i></a>, Hlong <a href="#NumOutput"><i>NumOutput</i></a>, const HString&amp; <a href="#OutputFunction"><i>OutputFunction</i></a>, const HString&amp; <a href="#Preprocessing"><i>Preprocessing</i></a>, Hlong <a href="#NumComponents"><i>NumComponents</i></a>, Hlong <a href="#RandSeed"><i>RandSeed</i></a>)</code></p>
<p>
<code>void <a href="HClassMlp.html">HClassMlp</a>::<b>HClassMlp</b>(Hlong <a href="#NumInput"><i>NumInput</i></a>, Hlong <a href="#NumHidden"><i>NumHidden</i></a>, Hlong <a href="#NumOutput"><i>NumOutput</i></a>, const char* <a href="#OutputFunction"><i>OutputFunction</i></a>, const char* <a href="#Preprocessing"><i>Preprocessing</i></a>, Hlong <a href="#NumComponents"><i>NumComponents</i></a>, Hlong <a href="#RandSeed"><i>RandSeed</i></a>)</code></p>
<p>
<code>void <a href="HClassMlp.html">HClassMlp</a>::<b>HClassMlp</b>(Hlong <a href="#NumInput"><i>NumInput</i></a>, Hlong <a href="#NumHidden"><i>NumHidden</i></a>, Hlong <a href="#NumOutput"><i>NumOutput</i></a>, const wchar_t* <a href="#OutputFunction"><i>OutputFunction</i></a>, const wchar_t* <a href="#Preprocessing"><i>Preprocessing</i></a>, Hlong <a href="#NumComponents"><i>NumComponents</i></a>, Hlong <a href="#RandSeed"><i>RandSeed</i></a>)  <span class="signnote">
            (
            Windows only)
          </span></code></p>
<p>
<code>void <a href="HClassMlp.html">HClassMlp</a>::<b>CreateClassMlp</b>(Hlong <a href="#NumInput"><i>NumInput</i></a>, Hlong <a href="#NumHidden"><i>NumHidden</i></a>, Hlong <a href="#NumOutput"><i>NumOutput</i></a>, const HString&amp; <a href="#OutputFunction"><i>OutputFunction</i></a>, const HString&amp; <a href="#Preprocessing"><i>Preprocessing</i></a>, Hlong <a href="#NumComponents"><i>NumComponents</i></a>, Hlong <a href="#RandSeed"><i>RandSeed</i></a>)</code></p>
<p>
<code>void <a href="HClassMlp.html">HClassMlp</a>::<b>CreateClassMlp</b>(Hlong <a href="#NumInput"><i>NumInput</i></a>, Hlong <a href="#NumHidden"><i>NumHidden</i></a>, Hlong <a href="#NumOutput"><i>NumOutput</i></a>, const char* <a href="#OutputFunction"><i>OutputFunction</i></a>, const char* <a href="#Preprocessing"><i>Preprocessing</i></a>, Hlong <a href="#NumComponents"><i>NumComponents</i></a>, Hlong <a href="#RandSeed"><i>RandSeed</i></a>)</code></p>
<p>
<code>void <a href="HClassMlp.html">HClassMlp</a>::<b>CreateClassMlp</b>(Hlong <a href="#NumInput"><i>NumInput</i></a>, Hlong <a href="#NumHidden"><i>NumHidden</i></a>, Hlong <a href="#NumOutput"><i>NumOutput</i></a>, const wchar_t* <a href="#OutputFunction"><i>OutputFunction</i></a>, const wchar_t* <a href="#Preprocessing"><i>Preprocessing</i></a>, Hlong <a href="#NumComponents"><i>NumComponents</i></a>, Hlong <a href="#RandSeed"><i>RandSeed</i></a>)  <span class="signnote">
            (
            Windows only)
          </span></code></p>
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<p>
<code>static void <a href="HOperatorSet.html">HOperatorSet</a>.<b>CreateClassMlp</b>(<a href="HTuple.html">HTuple</a> <a href="#NumInput"><i>numInput</i></a>, <a href="HTuple.html">HTuple</a> <a href="#NumHidden"><i>numHidden</i></a>, <a href="HTuple.html">HTuple</a> <a href="#NumOutput"><i>numOutput</i></a>, <a href="HTuple.html">HTuple</a> <a href="#OutputFunction"><i>outputFunction</i></a>, <a href="HTuple.html">HTuple</a> <a href="#Preprocessing"><i>preprocessing</i></a>, <a href="HTuple.html">HTuple</a> <a href="#NumComponents"><i>numComponents</i></a>, <a href="HTuple.html">HTuple</a> <a href="#RandSeed"><i>randSeed</i></a>, out <a href="HTuple.html">HTuple</a> <a href="#MLPHandle"><i>MLPHandle</i></a>)</code></p>
<p>
<code>public <a href="HClassMlp.html">HClassMlp</a>(int <a href="#NumInput"><i>numInput</i></a>, int <a href="#NumHidden"><i>numHidden</i></a>, int <a href="#NumOutput"><i>numOutput</i></a>, string <a href="#OutputFunction"><i>outputFunction</i></a>, string <a href="#Preprocessing"><i>preprocessing</i></a>, int <a href="#NumComponents"><i>numComponents</i></a>, int <a href="#RandSeed"><i>randSeed</i></a>)</code></p>
<p>
<code>void <a href="HClassMlp.html">HClassMlp</a>.<b>CreateClassMlp</b>(int <a href="#NumInput"><i>numInput</i></a>, int <a href="#NumHidden"><i>numHidden</i></a>, int <a href="#NumOutput"><i>numOutput</i></a>, string <a href="#OutputFunction"><i>outputFunction</i></a>, string <a href="#Preprocessing"><i>preprocessing</i></a>, int <a href="#NumComponents"><i>numComponents</i></a>, int <a href="#RandSeed"><i>randSeed</i></a>)</code></p>
</div>
<div data-if="python" style="display:none;">
<p>
<code>def <b>create_class_mlp</b>(<a href="#NumInput"><i>num_input</i></a>: int, <a href="#NumHidden"><i>num_hidden</i></a>: int, <a href="#NumOutput"><i>num_output</i></a>: int, <a href="#OutputFunction"><i>output_function</i></a>: str, <a href="#Preprocessing"><i>preprocessing</i></a>: str, <a href="#NumComponents"><i>num_components</i></a>: int, <a href="#RandSeed"><i>rand_seed</i></a>: int) -&gt; HHandle</code></p>
</div>
<h2 id="sec_description">描述</h2>
<p><code><span data-if="hdevelop" style="display:inline">create_class_mlp</span><span data-if="c" style="display:none">create_class_mlp</span><span data-if="cpp" style="display:none">CreateClassMlp</span><span data-if="com" style="display:none">CreateClassMlp</span><span data-if="dotnet" style="display:none">CreateClassMlp</span><span data-if="python" style="display:none">create_class_mlp</span></code> creates a neural net in the form of a
multilayer perceptron (MLP), which can be used for classification or
regression (function approximation), depending on how
<a href="#OutputFunction"><i><code><span data-if="hdevelop" style="display:inline">OutputFunction</span><span data-if="c" style="display:none">OutputFunction</span><span data-if="cpp" style="display:none">OutputFunction</span><span data-if="com" style="display:none">OutputFunction</span><span data-if="dotnet" style="display:none">outputFunction</span><span data-if="python" style="display:none">output_function</span></code></i></a> is set.  The MLP consists of three layers:
an input layer with <a href="#NumInput"><i><code><span data-if="hdevelop" style="display:inline">NumInput</span><span data-if="c" style="display:none">NumInput</span><span data-if="cpp" style="display:none">NumInput</span><span data-if="com" style="display:none">NumInput</span><span data-if="dotnet" style="display:none">numInput</span><span data-if="python" style="display:none">num_input</span></code></i></a> input variables (units,
neurons), a hidden layer with <a href="#NumHidden"><i><code><span data-if="hdevelop" style="display:inline">NumHidden</span><span data-if="c" style="display:none">NumHidden</span><span data-if="cpp" style="display:none">NumHidden</span><span data-if="com" style="display:none">NumHidden</span><span data-if="dotnet" style="display:none">numHidden</span><span data-if="python" style="display:none">num_hidden</span></code></i></a> units, and an
output layer with <a href="#NumOutput"><i><code><span data-if="hdevelop" style="display:inline">NumOutput</span><span data-if="c" style="display:none">NumOutput</span><span data-if="cpp" style="display:none">NumOutput</span><span data-if="com" style="display:none">NumOutput</span><span data-if="dotnet" style="display:none">numOutput</span><span data-if="python" style="display:none">num_output</span></code></i></a> output variables.  The MLP
performs the following steps to calculate the activations
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using linear combinations of the variables in an analogous manner as
above:
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the MLP.
</p>
<p>The activation function used in the output layer can be determined
by setting <a href="#OutputFunction"><i><code><span data-if="hdevelop" style="display:inline">OutputFunction</span><span data-if="c" style="display:none">OutputFunction</span><span data-if="cpp" style="display:none">OutputFunction</span><span data-if="com" style="display:none">OutputFunction</span><span data-if="dotnet" style="display:none">outputFunction</span><span data-if="python" style="display:none">output_function</span></code></i></a>.  For <a href="#OutputFunction"><i><code><span data-if="hdevelop" style="display:inline">OutputFunction</span><span data-if="c" style="display:none">OutputFunction</span><span data-if="cpp" style="display:none">OutputFunction</span><span data-if="com" style="display:none">OutputFunction</span><span data-if="dotnet" style="display:none">outputFunction</span><span data-if="python" style="display:none">output_function</span></code></i></a> =
<i><span data-if="hdevelop" style="display:inline">'linear'</span><span data-if="c" style="display:none">"linear"</span><span data-if="cpp" style="display:none">"linear"</span><span data-if="com" style="display:none">"linear"</span><span data-if="dotnet" style="display:none">"linear"</span><span data-if="python" style="display:none">"linear"</span></i>, the data are simply copied:
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This type of activation function should be used for regression
problems (function approximation).  This activation function is not
suited for classification problems.
</p>
<p>For <a href="#OutputFunction"><i><code><span data-if="hdevelop" style="display:inline">OutputFunction</span><span data-if="c" style="display:none">OutputFunction</span><span data-if="cpp" style="display:none">OutputFunction</span><span data-if="com" style="display:none">OutputFunction</span><span data-if="dotnet" style="display:none">outputFunction</span><span data-if="python" style="display:none">output_function</span></code></i></a> = <i><span data-if="hdevelop" style="display:inline">'logistic'</span><span data-if="c" style="display:none">"logistic"</span><span data-if="cpp" style="display:none">"logistic"</span><span data-if="com" style="display:none">"logistic"</span><span data-if="dotnet" style="display:none">"logistic"</span><span data-if="python" style="display:none">"logistic"</span></i>, the activations
are computed as follows:
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This type of activation function should be used for classification
problems with multiple (<a href="#NumOutput"><i><code><span data-if="hdevelop" style="display:inline">NumOutput</span><span data-if="c" style="display:none">NumOutput</span><span data-if="cpp" style="display:none">NumOutput</span><span data-if="com" style="display:none">NumOutput</span><span data-if="dotnet" style="display:none">numOutput</span><span data-if="python" style="display:none">num_output</span></code></i></a>) independent logical
attributes as output.  This kind of classification problem is
relatively rare in practice.
</p>
<p>For <a href="#OutputFunction"><i><code><span data-if="hdevelop" style="display:inline">OutputFunction</span><span data-if="c" style="display:none">OutputFunction</span><span data-if="cpp" style="display:none">OutputFunction</span><span data-if="com" style="display:none">OutputFunction</span><span data-if="dotnet" style="display:none">outputFunction</span><span data-if="python" style="display:none">output_function</span></code></i></a> = <i><span data-if="hdevelop" style="display:inline">'softmax'</span><span data-if="c" style="display:none">"softmax"</span><span data-if="cpp" style="display:none">"softmax"</span><span data-if="com" style="display:none">"softmax"</span><span data-if="dotnet" style="display:none">"softmax"</span><span data-if="python" style="display:none">"softmax"</span></i>, the activations
are computed as follows:
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</p>
<p>This type of activation function should be used for common
classification problems with multiple (<a href="#NumOutput"><i><code><span data-if="hdevelop" style="display:inline">NumOutput</span><span data-if="c" style="display:none">NumOutput</span><span data-if="cpp" style="display:none">NumOutput</span><span data-if="com" style="display:none">NumOutput</span><span data-if="dotnet" style="display:none">numOutput</span><span data-if="python" style="display:none">num_output</span></code></i></a>) mutually
exclusive classes as output.  In particular, <a href="#OutputFunction"><i><code><span data-if="hdevelop" style="display:inline">OutputFunction</span><span data-if="c" style="display:none">OutputFunction</span><span data-if="cpp" style="display:none">OutputFunction</span><span data-if="com" style="display:none">OutputFunction</span><span data-if="dotnet" style="display:none">outputFunction</span><span data-if="python" style="display:none">output_function</span></code></i></a>
= <i><span data-if="hdevelop" style="display:inline">'softmax'</span><span data-if="c" style="display:none">"softmax"</span><span data-if="cpp" style="display:none">"softmax"</span><span data-if="com" style="display:none">"softmax"</span><span data-if="dotnet" style="display:none">"softmax"</span><span data-if="python" style="display:none">"softmax"</span></i> must be used for the classification of pixel
data with <a href="classify_image_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">classify_image_class_mlp</span><span data-if="c" style="display:none">classify_image_class_mlp</span><span data-if="cpp" style="display:none">ClassifyImageClassMlp</span><span data-if="com" style="display:none">ClassifyImageClassMlp</span><span data-if="dotnet" style="display:none">ClassifyImageClassMlp</span><span data-if="python" style="display:none">classify_image_class_mlp</span></code></a>.
</p>
<p>The parameters <a href="#Preprocessing"><i><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></i></a> and <a href="#NumComponents"><i><code><span data-if="hdevelop" style="display:inline">NumComponents</span><span data-if="c" style="display:none">NumComponents</span><span data-if="cpp" style="display:none">NumComponents</span><span data-if="com" style="display:none">NumComponents</span><span data-if="dotnet" style="display:none">numComponents</span><span data-if="python" style="display:none">num_components</span></code></i></a> can
be used to specify a preprocessing of the feature vectors.  For
<a href="#Preprocessing"><i><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></i></a> = <i><span data-if="hdevelop" style="display:inline">'none'</span><span data-if="c" style="display:none">"none"</span><span data-if="cpp" style="display:none">"none"</span><span data-if="com" style="display:none">"none"</span><span data-if="dotnet" style="display:none">"none"</span><span data-if="python" style="display:none">"none"</span></i>, the feature vectors are
passed unaltered to the MLP.  <a href="#NumComponents"><i><code><span data-if="hdevelop" style="display:inline">NumComponents</span><span data-if="c" style="display:none">NumComponents</span><span data-if="cpp" style="display:none">NumComponents</span><span data-if="com" style="display:none">NumComponents</span><span data-if="dotnet" style="display:none">numComponents</span><span data-if="python" style="display:none">num_components</span></code></i></a> is ignored in
this case.
</p>
<p>For all other values of <a href="#Preprocessing"><i><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></i></a>, the training data
set is used to compute a transformation of the feature vectors
during the training as well as later in the classification or
evaluation.
</p>
<p>For <a href="#Preprocessing"><i><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></i></a> = <i><span data-if="hdevelop" style="display:inline">'normalization'</span><span data-if="c" style="display:none">"normalization"</span><span data-if="cpp" style="display:none">"normalization"</span><span data-if="com" style="display:none">"normalization"</span><span data-if="dotnet" style="display:none">"normalization"</span><span data-if="python" style="display:none">"normalization"</span></i>, the feature
vectors are normalized by subtracting the mean of the training
vectors and dividing the result by the standard deviation of the
individual components of the training vectors.  Hence, the
transformed feature vectors have a mean of 0 and a standard
deviation of 1.  The normalization does not change the length of the
feature vector.  <a href="#NumComponents"><i><code><span data-if="hdevelop" style="display:inline">NumComponents</span><span data-if="c" style="display:none">NumComponents</span><span data-if="cpp" style="display:none">NumComponents</span><span data-if="com" style="display:none">NumComponents</span><span data-if="dotnet" style="display:none">numComponents</span><span data-if="python" style="display:none">num_components</span></code></i></a> is ignored in this case.
This transformation can be used if the mean and standard deviation
of the feature vectors differs substantially from 0 and 1,
respectively, or for data in which the components of the feature
vectors are measured in different units (e.g., if some of the data
are gray value features and some are region features, or if region
features are mixed, e.g., <code>'circularity'</code> (unit: scalar) and
<code>'area'</code> (unit: pixel squared)).  In these cases, the training
of the net will typically require fewer iterations than without
normalization.
</p>
<p>For <a href="#Preprocessing"><i><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></i></a> = <i><span data-if="hdevelop" style="display:inline">'principal_components'</span><span data-if="c" style="display:none">"principal_components"</span><span data-if="cpp" style="display:none">"principal_components"</span><span data-if="com" style="display:none">"principal_components"</span><span data-if="dotnet" style="display:none">"principal_components"</span><span data-if="python" style="display:none">"principal_components"</span></i>, a
principal component analysis is performed.  First, the feature
vectors are normalized (see above).  Then, an orthogonal
transformation (a rotation in the feature space) that decorrelates
the training vectors is computed.  After the transformation, the
mean of the training vectors is 0 and the covariance matrix of the
training vectors is a diagonal matrix.  The transformation is chosen
such that the transformed features that contain the most variation
is contained in the first components of the transformed feature
vector.  With this, it is possible to omit the transformed features
in the last components of the feature vector, which typically are
mainly influenced by noise, without losing a large amount of
information.  The parameter <a href="#NumComponents"><i><code><span data-if="hdevelop" style="display:inline">NumComponents</span><span data-if="c" style="display:none">NumComponents</span><span data-if="cpp" style="display:none">NumComponents</span><span data-if="com" style="display:none">NumComponents</span><span data-if="dotnet" style="display:none">numComponents</span><span data-if="python" style="display:none">num_components</span></code></i></a> can be used to
determine how many of the transformed feature vector components
should be used.  Up to <a href="#NumInput"><i><code><span data-if="hdevelop" style="display:inline">NumInput</span><span data-if="c" style="display:none">NumInput</span><span data-if="cpp" style="display:none">NumInput</span><span data-if="com" style="display:none">NumInput</span><span data-if="dotnet" style="display:none">numInput</span><span data-if="python" style="display:none">num_input</span></code></i></a> components can be selected.
该算子 <a href="get_prep_info_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">get_prep_info_class_mlp</span><span data-if="c" style="display:none">get_prep_info_class_mlp</span><span data-if="cpp" style="display:none">GetPrepInfoClassMlp</span><span data-if="com" style="display:none">GetPrepInfoClassMlp</span><span data-if="dotnet" style="display:none">GetPrepInfoClassMlp</span><span data-if="python" style="display:none">get_prep_info_class_mlp</span></code></a> can be used to
determine how much information each transformed component contains.
Hence, it aids the selection of <a href="#NumComponents"><i><code><span data-if="hdevelop" style="display:inline">NumComponents</span><span data-if="c" style="display:none">NumComponents</span><span data-if="cpp" style="display:none">NumComponents</span><span data-if="com" style="display:none">NumComponents</span><span data-if="dotnet" style="display:none">numComponents</span><span data-if="python" style="display:none">num_components</span></code></i></a>.  Like data
normalization, this transformation can be used if the mean and
standard deviation of the feature vectors differs substantially from
0 and 1, respectively, or for feature vectors in which the
components of the data are measured in different units.  In
addition, this transformation is useful if it can be expected that
the features are highly correlated.
</p>
<p>In contrast to the above three transformations, which can be used
for all MLP types, the transformation specified by
<a href="#Preprocessing"><i><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></i></a> = <i><span data-if="hdevelop" style="display:inline">'canonical_variates'</span><span data-if="c" style="display:none">"canonical_variates"</span><span data-if="cpp" style="display:none">"canonical_variates"</span><span data-if="com" style="display:none">"canonical_variates"</span><span data-if="dotnet" style="display:none">"canonical_variates"</span><span data-if="python" style="display:none">"canonical_variates"</span></i> can only be
used if the MLP is used as a classifier with <a href="#OutputFunction"><i><code><span data-if="hdevelop" style="display:inline">OutputFunction</span><span data-if="c" style="display:none">OutputFunction</span><span data-if="cpp" style="display:none">OutputFunction</span><span data-if="com" style="display:none">OutputFunction</span><span data-if="dotnet" style="display:none">outputFunction</span><span data-if="python" style="display:none">output_function</span></code></i></a>
= <i><span data-if="hdevelop" style="display:inline">'softmax'</span><span data-if="c" style="display:none">"softmax"</span><span data-if="cpp" style="display:none">"softmax"</span><span data-if="com" style="display:none">"softmax"</span><span data-if="dotnet" style="display:none">"softmax"</span><span data-if="python" style="display:none">"softmax"</span></i>).  The computation of the canonical variates
is also called linear discriminant analysis.  In this case, a
transformation that first normalizes the training vectors and then
decorrelates the training vectors on average over all classes is
computed.  At the same time, the transformation maximally separates
the mean values of the individual classes.  As for
<a href="#Preprocessing"><i><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></i></a> = <i><span data-if="hdevelop" style="display:inline">'principal_components'</span><span data-if="c" style="display:none">"principal_components"</span><span data-if="cpp" style="display:none">"principal_components"</span><span data-if="com" style="display:none">"principal_components"</span><span data-if="dotnet" style="display:none">"principal_components"</span><span data-if="python" style="display:none">"principal_components"</span></i>, the
transformed components are sorted by information content, and hence
transformed components with little information content can be
omitted.  For canonical variates, up to min(<a href="#NumOutput"><i><code><span data-if="hdevelop" style="display:inline">NumOutput</span><span data-if="c" style="display:none">NumOutput</span><span data-if="cpp" style="display:none">NumOutput</span><span data-if="com" style="display:none">NumOutput</span><span data-if="dotnet" style="display:none">numOutput</span><span data-if="python" style="display:none">num_output</span></code></i></a> -
1, <a href="#NumInput"><i><code><span data-if="hdevelop" style="display:inline">NumInput</span><span data-if="c" style="display:none">NumInput</span><span data-if="cpp" style="display:none">NumInput</span><span data-if="com" style="display:none">NumInput</span><span data-if="dotnet" style="display:none">numInput</span><span data-if="python" style="display:none">num_input</span></code></i></a>) components can be selected.  Also in this
case, the information content of the transformed components can be
determined with <a href="get_prep_info_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">get_prep_info_class_mlp</span><span data-if="c" style="display:none">get_prep_info_class_mlp</span><span data-if="cpp" style="display:none">GetPrepInfoClassMlp</span><span data-if="com" style="display:none">GetPrepInfoClassMlp</span><span data-if="dotnet" style="display:none">GetPrepInfoClassMlp</span><span data-if="python" style="display:none">get_prep_info_class_mlp</span></code></a>.  Like principal
component analysis, canonical variates can be used to reduce the
amount of data without losing a large amount of information, while
additionally optimizing the separability of the classes after the
data reduction.
</p>
<p>For the last two types of transformations
(<i><span data-if="hdevelop" style="display:inline">'principal_components'</span><span data-if="c" style="display:none">"principal_components"</span><span data-if="cpp" style="display:none">"principal_components"</span><span data-if="com" style="display:none">"principal_components"</span><span data-if="dotnet" style="display:none">"principal_components"</span><span data-if="python" style="display:none">"principal_components"</span></i> and <i><span data-if="hdevelop" style="display:inline">'canonical_variates'</span><span data-if="c" style="display:none">"canonical_variates"</span><span data-if="cpp" style="display:none">"canonical_variates"</span><span data-if="com" style="display:none">"canonical_variates"</span><span data-if="dotnet" style="display:none">"canonical_variates"</span><span data-if="python" style="display:none">"canonical_variates"</span></i>),
the actual number of input units of the MLP is determined by
<a href="#NumComponents"><i><code><span data-if="hdevelop" style="display:inline">NumComponents</span><span data-if="c" style="display:none">NumComponents</span><span data-if="cpp" style="display:none">NumComponents</span><span data-if="com" style="display:none">NumComponents</span><span data-if="dotnet" style="display:none">numComponents</span><span data-if="python" style="display:none">num_components</span></code></i></a>, whereas <a href="#NumInput"><i><code><span data-if="hdevelop" style="display:inline">NumInput</span><span data-if="c" style="display:none">NumInput</span><span data-if="cpp" style="display:none">NumInput</span><span data-if="com" style="display:none">NumInput</span><span data-if="dotnet" style="display:none">numInput</span><span data-if="python" style="display:none">num_input</span></code></i></a> determines the
dimensionality of the input data (i.e., the length of the
untransformed feature vector).  Hence, by using one of these two
transformations, the number of input variables, and thus usually
also the number of hidden units can be reduced.  With this, the time
needed to train the MLP and to evaluate and classify a feature
vector is typically reduced.
</p>
<p>Usually, <a href="#NumHidden"><i><code><span data-if="hdevelop" style="display:inline">NumHidden</span><span data-if="c" style="display:none">NumHidden</span><span data-if="cpp" style="display:none">NumHidden</span><span data-if="com" style="display:none">NumHidden</span><span data-if="dotnet" style="display:none">numHidden</span><span data-if="python" style="display:none">num_hidden</span></code></i></a> should be selected in the order of
magnitude of <a href="#NumInput"><i><code><span data-if="hdevelop" style="display:inline">NumInput</span><span data-if="c" style="display:none">NumInput</span><span data-if="cpp" style="display:none">NumInput</span><span data-if="com" style="display:none">NumInput</span><span data-if="dotnet" style="display:none">numInput</span><span data-if="python" style="display:none">num_input</span></code></i></a> and <a href="#NumOutput"><i><code><span data-if="hdevelop" style="display:inline">NumOutput</span><span data-if="c" style="display:none">NumOutput</span><span data-if="cpp" style="display:none">NumOutput</span><span data-if="com" style="display:none">NumOutput</span><span data-if="dotnet" style="display:none">numOutput</span><span data-if="python" style="display:none">num_output</span></code></i></a>.  In many
cases, much smaller values of <a href="#NumHidden"><i><code><span data-if="hdevelop" style="display:inline">NumHidden</span><span data-if="c" style="display:none">NumHidden</span><span data-if="cpp" style="display:none">NumHidden</span><span data-if="com" style="display:none">NumHidden</span><span data-if="dotnet" style="display:none">numHidden</span><span data-if="python" style="display:none">num_hidden</span></code></i></a> already lead to
very good classification results.  If <a href="#NumHidden"><i><code><span data-if="hdevelop" style="display:inline">NumHidden</span><span data-if="c" style="display:none">NumHidden</span><span data-if="cpp" style="display:none">NumHidden</span><span data-if="com" style="display:none">NumHidden</span><span data-if="dotnet" style="display:none">numHidden</span><span data-if="python" style="display:none">num_hidden</span></code></i></a> is chosen
too large, the MLP may overfit the training data, which typically
leads to bad generalization properties, i.e., the MLP learns the
training data very well, but does not return very good results on
unknown data.
</p>
<p><code><span data-if="hdevelop" style="display:inline">create_class_mlp</span><span data-if="c" style="display:none">create_class_mlp</span><span data-if="cpp" style="display:none">CreateClassMlp</span><span data-if="com" style="display:none">CreateClassMlp</span><span data-if="dotnet" style="display:none">CreateClassMlp</span><span data-if="python" style="display:none">create_class_mlp</span></code> initializes the above described weights
with random numbers.  To ensure that the results of training the
classifier with <a href="train_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">train_class_mlp</span><span data-if="c" style="display:none">train_class_mlp</span><span data-if="cpp" style="display:none">TrainClassMlp</span><span data-if="com" style="display:none">TrainClassMlp</span><span data-if="dotnet" style="display:none">TrainClassMlp</span><span data-if="python" style="display:none">train_class_mlp</span></code></a> are reproducible, the seed
value of the random number generator is passed in <a href="#RandSeed"><i><code><span data-if="hdevelop" style="display:inline">RandSeed</span><span data-if="c" style="display:none">RandSeed</span><span data-if="cpp" style="display:none">RandSeed</span><span data-if="com" style="display:none">RandSeed</span><span data-if="dotnet" style="display:none">randSeed</span><span data-if="python" style="display:none">rand_seed</span></code></i></a>.
If the training results in a relatively large error, it sometimes
may be possible to achieve a smaller error by selecting a different
value for <a href="#RandSeed"><i><code><span data-if="hdevelop" style="display:inline">RandSeed</span><span data-if="c" style="display:none">RandSeed</span><span data-if="cpp" style="display:none">RandSeed</span><span data-if="com" style="display:none">RandSeed</span><span data-if="dotnet" style="display:none">randSeed</span><span data-if="python" style="display:none">rand_seed</span></code></i></a> and retraining an MLP.
</p>
<p>After the MLP has been created, typically training samples are added
to the MLP by repeatedly calling <a href="add_sample_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">add_sample_class_mlp</span><span data-if="c" style="display:none">add_sample_class_mlp</span><span data-if="cpp" style="display:none">AddSampleClassMlp</span><span data-if="com" style="display:none">AddSampleClassMlp</span><span data-if="dotnet" style="display:none">AddSampleClassMlp</span><span data-if="python" style="display:none">add_sample_class_mlp</span></code></a> or
<a href="read_samples_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">read_samples_class_mlp</span><span data-if="c" style="display:none">read_samples_class_mlp</span><span data-if="cpp" style="display:none">ReadSamplesClassMlp</span><span data-if="com" style="display:none">ReadSamplesClassMlp</span><span data-if="dotnet" style="display:none">ReadSamplesClassMlp</span><span data-if="python" style="display:none">read_samples_class_mlp</span></code></a>.  After this, the MLP is typically
trained using <a href="train_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">train_class_mlp</span><span data-if="c" style="display:none">train_class_mlp</span><span data-if="cpp" style="display:none">TrainClassMlp</span><span data-if="com" style="display:none">TrainClassMlp</span><span data-if="dotnet" style="display:none">TrainClassMlp</span><span data-if="python" style="display:none">train_class_mlp</span></code></a>.  Hereafter, the MLP can be
saved using <a href="write_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">write_class_mlp</span><span data-if="c" style="display:none">write_class_mlp</span><span data-if="cpp" style="display:none">WriteClassMlp</span><span data-if="com" style="display:none">WriteClassMlp</span><span data-if="dotnet" style="display:none">WriteClassMlp</span><span data-if="python" style="display:none">write_class_mlp</span></code></a>.  Alternatively, the MLP can be
used immediately after training to evaluate data using
<a href="evaluate_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">evaluate_class_mlp</span><span data-if="c" style="display:none">evaluate_class_mlp</span><span data-if="cpp" style="display:none">EvaluateClassMlp</span><span data-if="com" style="display:none">EvaluateClassMlp</span><span data-if="dotnet" style="display:none">EvaluateClassMlp</span><span data-if="python" style="display:none">evaluate_class_mlp</span></code></a> or, if the MLP is used as a classifier
(i.e., for <a href="#OutputFunction"><i><code><span data-if="hdevelop" style="display:inline">OutputFunction</span><span data-if="c" style="display:none">OutputFunction</span><span data-if="cpp" style="display:none">OutputFunction</span><span data-if="com" style="display:none">OutputFunction</span><span data-if="dotnet" style="display:none">outputFunction</span><span data-if="python" style="display:none">output_function</span></code></i></a> = <i><span data-if="hdevelop" style="display:inline">'softmax'</span><span data-if="c" style="display:none">"softmax"</span><span data-if="cpp" style="display:none">"softmax"</span><span data-if="com" style="display:none">"softmax"</span><span data-if="dotnet" style="display:none">"softmax"</span><span data-if="python" style="display:none">"softmax"</span></i>), to
classify data using <a href="classify_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">classify_class_mlp</span><span data-if="c" style="display:none">classify_class_mlp</span><span data-if="cpp" style="display:none">ClassifyClassMlp</span><span data-if="com" style="display:none">ClassifyClassMlp</span><span data-if="dotnet" style="display:none">ClassifyClassMlp</span><span data-if="python" style="display:none">classify_class_mlp</span></code></a>.
</p>
<p>The training of the MLP will usually result in very sharp boundaries
between the different classes, i.e., the confidence for one class
will drop from close to 1 (within the region of the class) to close
to 0 (within the region of a different class) within a very narrow
“band” in the feature space.  If the classes do not overlap, this
transition happens at a suitable location between the classes; if
the classes overlap, the transition happens at a suitable location
within the overlapping area.  While this sharp transition is
desirable in many applications, in some applications a smoother
transition between different classes (i.e., a transition within a
wider “band” in the feature space) is desirable to reflect a level
of uncertainty within the region in the feature space between the
classes.  Furthermore, as described above, it may be desirable to
prevent overfitting of the MLP to the training data.  For these
purposes, the MLP can be regularized by using
<a href="set_regularization_params_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">set_regularization_params_class_mlp</span><span data-if="c" style="display:none">set_regularization_params_class_mlp</span><span data-if="cpp" style="display:none">SetRegularizationParamsClassMlp</span><span data-if="com" style="display:none">SetRegularizationParamsClassMlp</span><span data-if="dotnet" style="display:none">SetRegularizationParamsClassMlp</span><span data-if="python" style="display:none">set_regularization_params_class_mlp</span></code></a>.
</p>
<p>An MLP, as defined above, has no inherent capability for novelty
detection, i.e., it will classify a random feature vector into one
of the classes with a confidence close to 1 (unless the random
feature vector happens to lie in a region of the feature space in
which the training samples of different classes overlap).  In some
applications, however, it is desirable to reject feature vectors
that do not lie close to any class, where “closesness” defined by
the proximity of the feature vector to the collection of feature
vectors in the training set.  To provide an MLP with the ability for
novelty detection, i.e., to reject feature vectors that do not
belong to any class, an explicit rejection class can be created by
setting <a href="#NumOutput"><i><code><span data-if="hdevelop" style="display:inline">NumOutput</span><span data-if="c" style="display:none">NumOutput</span><span data-if="cpp" style="display:none">NumOutput</span><span data-if="com" style="display:none">NumOutput</span><span data-if="dotnet" style="display:none">numOutput</span><span data-if="python" style="display:none">num_output</span></code></i></a> to the number of actual classes plus 1.
Then, <a href="set_rejection_params_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">set_rejection_params_class_mlp</span><span data-if="c" style="display:none">set_rejection_params_class_mlp</span><span data-if="cpp" style="display:none">SetRejectionParamsClassMlp</span><span data-if="com" style="display:none">SetRejectionParamsClassMlp</span><span data-if="dotnet" style="display:none">SetRejectionParamsClassMlp</span><span data-if="python" style="display:none">set_rejection_params_class_mlp</span></code></a> can be used to
configure <a href="train_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">train_class_mlp</span><span data-if="c" style="display:none">train_class_mlp</span><span data-if="cpp" style="display:none">TrainClassMlp</span><span data-if="com" style="display:none">TrainClassMlp</span><span data-if="dotnet" style="display:none">TrainClassMlp</span><span data-if="python" style="display:none">train_class_mlp</span></code></a> to automatically generate samples
for this rejection class.
</p>
<p>The combination of regularization and an automatic generation of a
rejection class is useful in many applications since it provides a
smooth transition between the actual classes and from the actual
classes to the rejection class.  This reflects the requirement of
these applications that only feature vectors within the area of the
feature space that corresponds to the training samples of each class
should have a confidence close to 1, whereas random feature vectors
not belonging to any class should have a confidence close to 0, and
that transitions between the classes should be smooth, reflecting a
growing degree of uncertainty the farther a feature vector lies from
the respective class.  In particular, OCR applications sometimes
have this requirement (see <a href="create_ocr_class_mlp.html"><code><span data-if="hdevelop" style="display:inline">create_ocr_class_mlp</span><span data-if="c" style="display:none">create_ocr_class_mlp</span><span data-if="cpp" style="display:none">CreateOcrClassMlp</span><span data-if="com" style="display:none">CreateOcrClassMlp</span><span data-if="dotnet" style="display:none">CreateOcrClassMlp</span><span data-if="python" style="display:none">create_ocr_class_mlp</span></code></a>).
</p>
<p>A comparison of the MLP and the support vector machine (SVM) (see
<a href="create_class_svm.html"><code><span data-if="hdevelop" style="display:inline">create_class_svm</span><span data-if="c" style="display:none">create_class_svm</span><span data-if="cpp" style="display:none">CreateClassSvm</span><span data-if="com" style="display:none">CreateClassSvm</span><span data-if="dotnet" style="display:none">CreateClassSvm</span><span data-if="python" style="display:none">create_class_svm</span></code></a>) typically shows that SVMs are generally
faster at training, especially for huge training sets, and achieve
slightly better recognition rates than MLPs.  The MLP is faster at
classification and should therefore be preferred in time critical
applications.  Please note that this guideline assumes optimal
tuning of the parameters.</p>
<h2 id="sec_execution">运行信息</h2>
<ul>
  <li>多线程类型:可重入(与非独占操作符并行运行)。</li>
<li>多线程作用域:全局(可以从任何线程调用)。</li>
  <li>未经并行化处理。</li>
</ul>
<p>This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.</p>
<h2 id="sec_parameters">参数表</h2>
  <div class="par">
<div class="parhead">
<span id="NumInput" class="parname"><b><code><span data-if="hdevelop" style="display:inline">NumInput</span><span data-if="c" style="display:none">NumInput</span><span data-if="cpp" style="display:none">NumInput</span><span data-if="com" style="display:none">NumInput</span><span data-if="dotnet" style="display:none">numInput</span><span data-if="python" style="display:none">num_input</span></code></b> (input_control)  </span><span>integer <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">int</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (integer)</span><span data-if="dotnet" style="display:none"> (<i>int</i> / </span><span data-if="dotnet" style="display:none">long)</span><span data-if="cpp" style="display:none"> (<i>Hlong</i>)</span><span data-if="c" style="display:none"> (<i>Hlong</i>)</span></span>
</div>
<p class="pardesc">Number of input variables (features) of the MLP.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>20</p>
<p class="pardesc"><span class="parcat">Suggested values:
      </span>1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100</p>
<p class="pardesc"><span class="parcat">Restriction:
      </span><code>NumInput &gt;= 1</code></p>
</div>
  <div class="par">
<div class="parhead">
<span id="NumHidden" class="parname"><b><code><span data-if="hdevelop" style="display:inline">NumHidden</span><span data-if="c" style="display:none">NumHidden</span><span data-if="cpp" style="display:none">NumHidden</span><span data-if="com" style="display:none">NumHidden</span><span data-if="dotnet" style="display:none">numHidden</span><span data-if="python" style="display:none">num_hidden</span></code></b> (input_control)  </span><span>integer <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">int</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (integer)</span><span data-if="dotnet" style="display:none"> (<i>int</i> / </span><span data-if="dotnet" style="display:none">long)</span><span data-if="cpp" style="display:none"> (<i>Hlong</i>)</span><span data-if="c" style="display:none"> (<i>Hlong</i>)</span></span>
</div>
<p class="pardesc">Number of hidden units of the MLP.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>10</p>
<p class="pardesc"><span class="parcat">Suggested values:
      </span>1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150</p>
<p class="pardesc"><span class="parcat">Restriction:
      </span><code>NumHidden &gt;= 1</code></p>
</div>
  <div class="par">
<div class="parhead">
<span id="NumOutput" class="parname"><b><code><span data-if="hdevelop" style="display:inline">NumOutput</span><span data-if="c" style="display:none">NumOutput</span><span data-if="cpp" style="display:none">NumOutput</span><span data-if="com" style="display:none">NumOutput</span><span data-if="dotnet" style="display:none">numOutput</span><span data-if="python" style="display:none">num_output</span></code></b> (input_control)  </span><span>integer <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">int</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (integer)</span><span data-if="dotnet" style="display:none"> (<i>int</i> / </span><span data-if="dotnet" style="display:none">long)</span><span data-if="cpp" style="display:none"> (<i>Hlong</i>)</span><span data-if="c" style="display:none"> (<i>Hlong</i>)</span></span>
</div>
<p class="pardesc">Number of output variables (classes) of the MLP.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>5</p>
<p class="pardesc"><span class="parcat">Suggested values:
      </span>1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150</p>
<p class="pardesc"><span class="parcat">Restriction:
      </span><code>NumOutput &gt;= 1</code></p>
</div>
  <div class="par">
<div class="parhead">
<span id="OutputFunction" class="parname"><b><code><span data-if="hdevelop" style="display:inline">OutputFunction</span><span data-if="c" style="display:none">OutputFunction</span><span data-if="cpp" style="display:none">OutputFunction</span><span data-if="com" style="display:none">OutputFunction</span><span data-if="dotnet" style="display:none">outputFunction</span><span data-if="python" style="display:none">output_function</span></code></b> (input_control)  </span><span>string <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">str</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (string)</span><span data-if="dotnet" style="display:none"> (<i>string</i>)</span><span data-if="cpp" style="display:none"> (<i>HString</i>)</span><span data-if="c" style="display:none"> (<i>char*</i>)</span></span>
</div>
<p class="pardesc">Type of the activation function in the output
layer of the MLP.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>
    <span data-if="hdevelop" style="display:inline">'softmax'</span>
    <span data-if="c" style="display:none">"softmax"</span>
    <span data-if="cpp" style="display:none">"softmax"</span>
    <span data-if="com" style="display:none">"softmax"</span>
    <span data-if="dotnet" style="display:none">"softmax"</span>
    <span data-if="python" style="display:none">"softmax"</span>
</p>
<p class="pardesc"><span class="parcat">List of values:
      </span><span data-if="hdevelop" style="display:inline">'linear'</span><span data-if="c" style="display:none">"linear"</span><span data-if="cpp" style="display:none">"linear"</span><span data-if="com" style="display:none">"linear"</span><span data-if="dotnet" style="display:none">"linear"</span><span data-if="python" style="display:none">"linear"</span>, <span data-if="hdevelop" style="display:inline">'logistic'</span><span data-if="c" style="display:none">"logistic"</span><span data-if="cpp" style="display:none">"logistic"</span><span data-if="com" style="display:none">"logistic"</span><span data-if="dotnet" style="display:none">"logistic"</span><span data-if="python" style="display:none">"logistic"</span>, <span data-if="hdevelop" style="display:inline">'softmax'</span><span data-if="c" style="display:none">"softmax"</span><span data-if="cpp" style="display:none">"softmax"</span><span data-if="com" style="display:none">"softmax"</span><span data-if="dotnet" style="display:none">"softmax"</span><span data-if="python" style="display:none">"softmax"</span></p>
</div>
  <div class="par">
<div class="parhead">
<span id="Preprocessing" class="parname"><b><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></b> (input_control)  </span><span>string <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">str</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (string)</span><span data-if="dotnet" style="display:none"> (<i>string</i>)</span><span data-if="cpp" style="display:none"> (<i>HString</i>)</span><span data-if="c" style="display:none"> (<i>char*</i>)</span></span>
</div>
<p class="pardesc">Type of preprocessing used to transform the
feature vectors.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>
    <span data-if="hdevelop" style="display:inline">'normalization'</span>
    <span data-if="c" style="display:none">"normalization"</span>
    <span data-if="cpp" style="display:none">"normalization"</span>
    <span data-if="com" style="display:none">"normalization"</span>
    <span data-if="dotnet" style="display:none">"normalization"</span>
    <span data-if="python" style="display:none">"normalization"</span>
</p>
<p class="pardesc"><span class="parcat">List of values:
      </span><span data-if="hdevelop" style="display:inline">'canonical_variates'</span><span data-if="c" style="display:none">"canonical_variates"</span><span data-if="cpp" style="display:none">"canonical_variates"</span><span data-if="com" style="display:none">"canonical_variates"</span><span data-if="dotnet" style="display:none">"canonical_variates"</span><span data-if="python" style="display:none">"canonical_variates"</span>, <span data-if="hdevelop" style="display:inline">'none'</span><span data-if="c" style="display:none">"none"</span><span data-if="cpp" style="display:none">"none"</span><span data-if="com" style="display:none">"none"</span><span data-if="dotnet" style="display:none">"none"</span><span data-if="python" style="display:none">"none"</span>, <span data-if="hdevelop" style="display:inline">'normalization'</span><span data-if="c" style="display:none">"normalization"</span><span data-if="cpp" style="display:none">"normalization"</span><span data-if="com" style="display:none">"normalization"</span><span data-if="dotnet" style="display:none">"normalization"</span><span data-if="python" style="display:none">"normalization"</span>, <span data-if="hdevelop" style="display:inline">'principal_components'</span><span data-if="c" style="display:none">"principal_components"</span><span data-if="cpp" style="display:none">"principal_components"</span><span data-if="com" style="display:none">"principal_components"</span><span data-if="dotnet" style="display:none">"principal_components"</span><span data-if="python" style="display:none">"principal_components"</span></p>
</div>
  <div class="par">
<div class="parhead">
<span id="NumComponents" class="parname"><b><code><span data-if="hdevelop" style="display:inline">NumComponents</span><span data-if="c" style="display:none">NumComponents</span><span data-if="cpp" style="display:none">NumComponents</span><span data-if="com" style="display:none">NumComponents</span><span data-if="dotnet" style="display:none">numComponents</span><span data-if="python" style="display:none">num_components</span></code></b> (input_control)  </span><span>integer <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">int</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (integer)</span><span data-if="dotnet" style="display:none"> (<i>int</i> / </span><span data-if="dotnet" style="display:none">long)</span><span data-if="cpp" style="display:none"> (<i>Hlong</i>)</span><span data-if="c" style="display:none"> (<i>Hlong</i>)</span></span>
</div>
<p class="pardesc">Preprocessing parameter: Number of transformed
features (ignored for <a href="#Preprocessing"><i><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></i></a> =
<i><span data-if="hdevelop" style="display:inline">'none'</span><span data-if="c" style="display:none">"none"</span><span data-if="cpp" style="display:none">"none"</span><span data-if="com" style="display:none">"none"</span><span data-if="dotnet" style="display:none">"none"</span><span data-if="python" style="display:none">"none"</span></i> and <a href="#Preprocessing"><i><code><span data-if="hdevelop" style="display:inline">Preprocessing</span><span data-if="c" style="display:none">Preprocessing</span><span data-if="cpp" style="display:none">Preprocessing</span><span data-if="com" style="display:none">Preprocessing</span><span data-if="dotnet" style="display:none">preprocessing</span><span data-if="python" style="display:none">preprocessing</span></code></i></a> =
<i><span data-if="hdevelop" style="display:inline">'normalization'</span><span data-if="c" style="display:none">"normalization"</span><span data-if="cpp" style="display:none">"normalization"</span><span data-if="com" style="display:none">"normalization"</span><span data-if="dotnet" style="display:none">"normalization"</span><span data-if="python" style="display:none">"normalization"</span></i>).</p>
<p class="pardesc"><span class="parcat">Default:
      </span>10</p>
<p class="pardesc"><span class="parcat">Suggested values:
      </span>1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100</p>
<p class="pardesc"><span class="parcat">Restriction:
      </span><code>NumComponents &gt;= 1</code></p>
</div>
  <div class="par">
<div class="parhead">
<span id="RandSeed" class="parname"><b><code><span data-if="hdevelop" style="display:inline">RandSeed</span><span data-if="c" style="display:none">RandSeed</span><span data-if="cpp" style="display:none">RandSeed</span><span data-if="com" style="display:none">RandSeed</span><span data-if="dotnet" style="display:none">randSeed</span><span data-if="python" style="display:none">rand_seed</span></code></b> (input_control)  </span><span>integer <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">int</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (integer)</span><span data-if="dotnet" style="display:none"> (<i>int</i> / </span><span data-if="dotnet" style="display:none">long)</span><span data-if="cpp" style="display:none"> (<i>Hlong</i>)</span><span data-if="c" style="display:none"> (<i>Hlong</i>)</span></span>
</div>
<p class="pardesc">Seed value of the random number generator that
is used to initialize the MLP with random values.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>42</p>
</div>
  <div class="par">
<div class="parhead">
<span id="MLPHandle" class="parname"><b><code><span data-if="hdevelop" style="display:inline">MLPHandle</span><span data-if="c" style="display:none">MLPHandle</span><span data-if="cpp" style="display:none">MLPHandle</span><span data-if="com" style="display:none">MLPHandle</span><span data-if="dotnet" style="display:none">MLPHandle</span><span data-if="python" style="display:none">mlphandle</span></code></b> (output_control)  </span><span>class_mlp <code>→</code> <span data-if="dotnet" style="display:none"><a href="HClassMlp.html">HClassMlp</a>, </span><span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">HHandle</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (handle)</span><span data-if="dotnet" style="display:none"> (<i>IntPtr</i>)</span><span data-if="cpp" style="display:none"> (<i>HHandle</i>)</span><span data-if="c" style="display:none"> (<i>handle</i>)</span></span>
</div>
<p class="pardesc">MLP handle.</p>
</div>
<h2 id="sec_example_all">例程 (HDevelop)</h2>
<pre class="example">
* Use the MLP for regression (function approximation)
create_class_mlp (1, NumHidden, 1, 'linear', 'none', 1, 42, MLPHandle)
* Generate the training data
* D = [...]
* T = [...]
* Add the training data
for J := 0 to NumData-1 by 1
    add_sample_class_mlp (MLPHandle, D[J], T[J])
endfor
* Train the MLP
train_class_mlp (MLPHandle, 200, 0.001, 0.001, Error, ErrorLog)
* Generate test data
* X = [...]
* Compute the output of the MLP on the test data
for J := 0 to N-1 by 1
    evaluate_class_mlp (MLPHandle, X[J], Y)
endfor

* Use the MLP for classification
create_class_mlp (NumIn, NumHidden, NumOut, 'softmax', \
                  'normalization', NumIn, 42, MLPHandle)
* Generate and add the training data
for J := 0 to NumData-1 by 1
    * Generate training features and classes
    * Data = [...]
    * Class = [...]
    add_sample_class_mlp (MLPHandle, Data, Class)
endfor
* Train the MLP
train_class_mlp (MLPHandle, 100, 1, 0.01, Error, ErrorLog)
* Use the MLP to classify unknown data
for J := 0 to N-1 by 1
    * Extract features
    * Features = [...]
    classify_class_mlp (MLPHandle, Features, 1, Class, Confidence)
endfor
</pre>
<h2 id="sec_result">结果</h2>
<p>如果参数均有效，算子 <code><span data-if="hdevelop" style="display:inline">create_class_mlp</span><span data-if="c" style="display:none">create_class_mlp</span><span data-if="cpp" style="display:none">CreateClassMlp</span><span data-if="com" style="display:none">CreateClassMlp</span><span data-if="dotnet" style="display:none">CreateClassMlp</span><span data-if="python" style="display:none">create_class_mlp</span></code>
返回值 <TT>2</TT> (
      <TT>H_MSG_TRUE</TT>)
    .  If necessary, an exception is
raised.</p>
<h2 id="sec_successors">可能的后置算子</h2>
<p>
<code><a href="add_sample_class_mlp.html"><span data-if="hdevelop" style="display:inline">add_sample_class_mlp</span><span data-if="c" style="display:none">add_sample_class_mlp</span><span data-if="cpp" style="display:none">AddSampleClassMlp</span><span data-if="com" style="display:none">AddSampleClassMlp</span><span data-if="dotnet" style="display:none">AddSampleClassMlp</span><span data-if="python" style="display:none">add_sample_class_mlp</span></a></code>, 
<code><a href="set_regularization_params_class_mlp.html"><span data-if="hdevelop" style="display:inline">set_regularization_params_class_mlp</span><span data-if="c" style="display:none">set_regularization_params_class_mlp</span><span data-if="cpp" style="display:none">SetRegularizationParamsClassMlp</span><span data-if="com" style="display:none">SetRegularizationParamsClassMlp</span><span data-if="dotnet" style="display:none">SetRegularizationParamsClassMlp</span><span data-if="python" style="display:none">set_regularization_params_class_mlp</span></a></code>, 
<code><a href="set_rejection_params_class_mlp.html"><span data-if="hdevelop" style="display:inline">set_rejection_params_class_mlp</span><span data-if="c" style="display:none">set_rejection_params_class_mlp</span><span data-if="cpp" style="display:none">SetRejectionParamsClassMlp</span><span data-if="com" style="display:none">SetRejectionParamsClassMlp</span><span data-if="dotnet" style="display:none">SetRejectionParamsClassMlp</span><span data-if="python" style="display:none">set_rejection_params_class_mlp</span></a></code>
</p>
<h2 id="sec_alternatives">可替代算子</h2>
<p>
<code><a href="read_dl_classifier.html"><span data-if="hdevelop" style="display:inline">read_dl_classifier</span><span data-if="c" style="display:none">read_dl_classifier</span><span data-if="cpp" style="display:none">ReadDlClassifier</span><span data-if="com" style="display:none">ReadDlClassifier</span><span data-if="dotnet" style="display:none">ReadDlClassifier</span><span data-if="python" style="display:none">read_dl_classifier</span></a></code>, 
<code><a href="create_class_svm.html"><span data-if="hdevelop" style="display:inline">create_class_svm</span><span data-if="c" style="display:none">create_class_svm</span><span data-if="cpp" style="display:none">CreateClassSvm</span><span data-if="com" style="display:none">CreateClassSvm</span><span data-if="dotnet" style="display:none">CreateClassSvm</span><span data-if="python" style="display:none">create_class_svm</span></a></code>, 
<code><a href="create_class_gmm.html"><span data-if="hdevelop" style="display:inline">create_class_gmm</span><span data-if="c" style="display:none">create_class_gmm</span><span data-if="cpp" style="display:none">CreateClassGmm</span><span data-if="com" style="display:none">CreateClassGmm</span><span data-if="dotnet" style="display:none">CreateClassGmm</span><span data-if="python" style="display:none">create_class_gmm</span></a></code>
</p>
<h2 id="sec_see">参考其它</h2>
<p>
<code><a href="clear_class_mlp.html"><span data-if="hdevelop" style="display:inline">clear_class_mlp</span><span data-if="c" style="display:none">clear_class_mlp</span><span data-if="cpp" style="display:none">ClearClassMlp</span><span data-if="com" style="display:none">ClearClassMlp</span><span data-if="dotnet" style="display:none">ClearClassMlp</span><span data-if="python" style="display:none">clear_class_mlp</span></a></code>, 
<code><a href="train_class_mlp.html"><span data-if="hdevelop" style="display:inline">train_class_mlp</span><span data-if="c" style="display:none">train_class_mlp</span><span data-if="cpp" style="display:none">TrainClassMlp</span><span data-if="com" style="display:none">TrainClassMlp</span><span data-if="dotnet" style="display:none">TrainClassMlp</span><span data-if="python" style="display:none">train_class_mlp</span></a></code>, 
<code><a href="classify_class_mlp.html"><span data-if="hdevelop" style="display:inline">classify_class_mlp</span><span data-if="c" style="display:none">classify_class_mlp</span><span data-if="cpp" style="display:none">ClassifyClassMlp</span><span data-if="com" style="display:none">ClassifyClassMlp</span><span data-if="dotnet" style="display:none">ClassifyClassMlp</span><span data-if="python" style="display:none">classify_class_mlp</span></a></code>, 
<code><a href="evaluate_class_mlp.html"><span data-if="hdevelop" style="display:inline">evaluate_class_mlp</span><span data-if="c" style="display:none">evaluate_class_mlp</span><span data-if="cpp" style="display:none">EvaluateClassMlp</span><span data-if="com" style="display:none">EvaluateClassMlp</span><span data-if="dotnet" style="display:none">EvaluateClassMlp</span><span data-if="python" style="display:none">evaluate_class_mlp</span></a></code>
</p>
<h2 id="sec_references">References</h2>
<p>

Christopher M. Bishop: “Neural Networks for Pattern Recognition”;
Oxford University Press, Oxford; 1995.
<br>
Andrew Webb: “Statistical Pattern Recognition”; Arnold, London;
1999.
</p>
<h2 id="sec_module">模块</h2>
<p>
Foundation</p>
<!--OP_REF_FOOTER_START-->
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